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Enregistrement W7110217118 · doi:10.1108/qrde-03-2021-0015

Summary

2021· article· en· W7110217118 sur OpenAlexaboutno aff

Notice bibliographique

RevueQuarterly review of distance education · 2021
Typearticle
Langueen
DomaineComputer Science
ThématiqueDigital Education and Society
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGovernment (linguistics)PandemicRepresentation (politics)Higher educationChinaTable (database)

Résumé

récupéré en direct d'OpenAlex

An online survey was developed to collect demographic, as well other data relevant to the focus of this study. The link to the survey was sent to the 45 Association for Educational Communications and Technology (AECT) members who had responded to the invitation sent to all AECT members, indicating their interest to participate in the study. Of this group, only 26 people (57.8%) completed the online survey. Table 1 shows a breakdown of the demographic characteristics of this sample.The majority who completed the survey were women (73.8%). The respondents were also mainly from the higher education sector (84.6%). Age wise, most of them (73.1%) were in the 30’s to 50’s age range. Eight of the 26 respondents (30.8%) were from institutions outside the United States with representation from almost every continent; Eurasia (Azerbaijan and Georgia), Africa (Nigeria), Asia (Indonesia), Europe (United Kingdom) and North America (Canada). In addition, the respondents also represent a myriad of perspectives, experiencing the impact of the global pandemic in a variety of roles. In higher education, among the respondents were doctoral students; instructional designers including those with administrative and academic duties related to instructional design; full-time faculty, some of whom also served in academic administrative roles; adjuncts, as well as a librarian who also serves as a liaison to one of the colleges on campus. In the K–12 arena, there were a few teachers bringing their perspectives from the elementary, middle and high school contexts. Finally, there was also representation from one of the departments in government bringing the perspective of an instructional designer and training management advisor on the impact of the pandemic on the execution of training programs.From those who completed the online survey, 13 participants (50%) submitted a narrative of their experience of the pandemic in the form of a research report, a journal log or a personal essay. A description of this smaller sample is presented in Table 2.The case study essays were a balance of perspectives from both males (46.2%) and females (53.8%) who submitted as the first author. They also were a good representation across the age groups, from 20’s to 70’s (see Table 2).It is important to highlight that 12 out of 13 (92.3%) are from individuals who are in various roles at higher education institutions, both public and private institutions. Also, 5 of the 13 are essays submitted by individuals in various roles at institutions abroad.In some essays, the focus is on a particular institution or even a particular college. Whereas, in other essays, although the author is from a higher education context, the perspectives shared is a bird’s eye view of the educational system in the country or the region and this may encompass both K–12 and higher education.The essays poignantly illustrate how different educational institutions, both in K–12 and in higher education located in the United States, as well as around the world had taken steps to continue to function as the unprecedented global pandemic caused by COVID-19 unfolded. Most of the essays cover the period between March to September 2020. Although the impact of this global phenomenon is described as viewed through the lenses of individuals in a variety of roles in various types of educational institutions, a few common themes and unique challenges that were identified are briefly discussed here.A number of the essays, from higher education faculty perspective make the observation that their institution seems to be caught offguard. The “order to move online came slowly with little or no cross-campus consultation”. Another essay from a doctoral student perspective makes the observation that the initial response was a sense of general disbelief that COVID-19 which started half way across the world in China could impact a university in the middle of the United States of America!Many of the essays identify regular communication regarding the impact of the pandemic on the functioning of the institution, the students and faculty, and steps being taken to ensure their safety was very much appreciated. However, the observation was also made that much of this communication was top-down with very little faculty and staff involvement in the decisionmaking process. Similarly in the K–12 setting, teacher and parents appreciated the communication and the transparency regarding the situation that was in flux but were very minimally involved in the decisions being made.Many of the essays highlight the adjustments that were made to the educational process, the academic calendar, assessments, and how both faculty and students were asked to be flexible and adaptable. For example, there was a 2-week break in the face-to-face teaching process, both in higher education and K–12 institutions to allow faculty and teachers who were teaching face-to-face to transition their teaching materials to an online format and prepare to pivot their teaching to an online format too. Similarly, the attendance policy was relaxed when it was found that students could not get access to the internet or that it was spotty or too slow. Many of the essays describe the process of trial and error as different strategies are put in place, then modified or abandoned when they are found to be ineffective and replaced with others.The response in many institutions seemed to be to purchase hardware and software licenses and make infrastructure adjustments to classrooms, lecture halls, and teaching laboratory spaces. Some wrote that inadequate investments were made in training and supporting faculty to teach fully online or in a hybrid or blendflex model. Although, some do highlight the pivotal role of the instructional designers and how in some higher education institutions, this resources was expanded and fully utilized.Some think that a silver lining to this pandemic is that educational institutions, whether in K–12, higher education or in the corporate or government sectors, have all been forced to adopt and engage in online teaching and learning. As a result, it has required instructional technologists to rethink the delivery of education and led to improving perceptions of online education.Does it then mean that online education is here to stay?The next question to ask would be, how will institutions use this experience to identify lessons learned? Will this painful experience result in contingency planning to better prepare for the next crisis become an integral part of educational institutions’ strategic plans?

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,408
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,000
Communication savante0,0040,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,5920,432

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,286
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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